Enhancing microRNA167A expression in seed decreases the α-linolenic acid content and increases seed size in Camelina sativa.

Despite well established roles of microRNAs in plant development, few aspects have been addressed to understand their effects in seeds especially on lipid metabolism. In this study, we showed that overexpressing microRNA167A (miR167OE) in camelina (Camelina sativa) under a seed-specific promoter changed fatty acid composition and increased seed size. Specifically, the miR167OE seeds had a lower α-linolenic acid with a concomitantly higher linoleic acid content than the wild-type. This decreased level of fatty acid desaturation corresponded to a decreased transcriptional expression of the camelina fatty acid desaturase3 (CsFAD3) in developing seeds. MiR167 targeted the transcription factor auxin response factor (CsARF8) in camelina, as had been reported previously in Arabidopsis. Chromatin immunoprecipitation experiments combined with transcriptome analysis indicated that CsARF8 bound to promoters of camelina bZIP67 and ABI3 genes. These transcription factors directly or through the ABI3-bZIP12 pathway regulate CsFAD3 expression and affect α-linolenic acid accumulation. In addition, to decipher the miR167A-CsARF8 mediated transcriptional cascade for CsFAD3 suppression, transcriptome analysis was conducted to implicate mechanisms that regulate seed size in camelina. Expression levels of many genes were altered in miR167OE, including orthologs that have previously been identified to affect seed size in other plants. Most notably, genes for seed coat development such as suberin and lignin biosynthesis were down-regulated. This study provides valuable insights into the regulatory mechanism of fatty acid metabolism and seed size determination, and suggests possible approaches to improve these important traits in camelina

Plasma Electrolytic Oxidation (PEO) Coatings on a Mg Alloy from Particle Containing Electrolytes

Plasma electrolytic oxidation (PEO) processing for Mg alloy is known for decades and has been established as a well-known industrial surface treatment offering a reasonable wear and corrosion protection. However the long-term protection is often limited by the intrinsic porosity and limited phase compositions in the PEO layer. A novel optimization approach is to introduce particles to the PEO electrolyte, aiming at their in-situ incorporation into PEO coatings during growth. The idea is that with the help of particles the defects can be sealed, and the composition range and the functionalities of produced coatings can be enhanced. The thesis reports the influence of particle addition on PEO processing, how the particle up-take can be controlled and how the morphology, microstructure, phase composition, and properties of PEO coating on Mg alloy are influenced. The mechanisms of uptake and incorporation of particles into PEO layers as well as the coating growth mechanisms are also discussed. It was found that addition of particles cannot avoid/seal fully the high porosity of PEO coatings. Moreover, the growth rate of the coatings is reduced in the presence of particles. The nature of particle itself, together with electrical and electrolyte parameters during the process determine the way and efficiency of particle uptake and incorporation into PEO coatings. The final incorporation of the particles into the coating can range from inert to reactive, which can also be controlled by modifying the processing parameters. Although the corrosion resistance of the coating cannot be significantly improved by the particles, it is feasible to control and modify the biodegradability and compatibility of the coating via addition of particles. Furthermore, multifunctional coatings with anti-wear and photocatalytic properties were produced and it was demonstrated that particle properties can be transferred directly to the coatings

The Modeling of Interval-Valued Time Series Using Possibility Measure-Based Encoding-Decoding Mechanism

Interval-valued time series (ITS) is a collection of interval-valued data whose entires are ordered by time. The modeling of ITS is an ongoing issue pursued by many researchers. There are diverse ITS models showing better performance. This paper proposes a new ITS model using possibility measure-based encoding-decoding mechanism involved in fuzzy theory. The proposed model consists of four modules, say, linguistic variable generation module, encoding module, inference module and decoding module. The linguistic variable generation module can provide a series of linguistic variables expressed in fuzzy sets used to described dynamic characteristics of ITS. The encoding module encodes ITS into some embedding vectors with semantics with the aid of possibility measure and linguistic variables formed by linguistic variable generation module. The inference module uses artificial neural network to capture relationship implied in those embedding vectors with semantic. The decoding module decodes for the outputs of the inference module to produce the output of linguistic and interval formats by using the possibility measure-based encoding-decoding mechanism. In comparison with existing ITS models, the proposed model can not only produce the output of linguistic format, but also exhibit better numeric performance

3D Framework Carbon for High-Performance Zinc-Ion Capacitors

Given the rapid progress and widespread adoption of advanced energy storage devices, there has been a growing interest in aqueous capacitors that offer non-flammable properties and high safety standards. Consequently, extensive research efforts have been dedicated to investigating zinc anodes and low-cost carbonaceous cathode materials. Despite these efforts, the development of high-performance zinc-ion capacitors (ZICs) still faces challenges, such as limited cycling stability and low energy densities. In this study, we present a novel approach to address these challenges. We introduce a three-dimensional (3D) conductive porous carbon framework cathode combined with zinc anode cells, which exhibit exceptional stability and durability in ZICs. Our experimental results reveal remarkable cycling performance, with a capacity retention of approximately 97.3% and a coulombic efficiency of nearly 100% even after 10,000 charge–discharge cycles. These findings represent significant progress in improving the performance of ZICs

Controllable Degradable Plasma Electrolytic Oxidation Coated Mg Alloy for Biomedical Application

A controllable degradable coating on Mg alloy based on plasma electrolytic oxidation (PEO) process is reported for the first time. The reported results show that introduction of silica nanoparticles into PEO electrolytes leads to their reactive incorporation in coatings and thus influencing the degradation behavior. Dissolution of amorphous phases facilitates chemical reaction with components of simulated body fluid, resulting in self-healing effect via redeposition of insoluble conversion products. The dynamic balance between dissolution of the original coating and reconstruction of corrosion layer is mainly determined by the phase composition of the coating as well as the surrounding corrosive medium

Core Challenges in Embodied Vision-Language Planning

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.Comment: 35 page

Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Comparison of 2D and 3D Plasma Electrolytic Oxidation (PEO)-Based Coating Porosity Data Obtained by X-ray Tomography Rendering and a Classical Metallographic Approach

In this work, the porosity of plasma electrolytic oxidation (PEO)-based coatings on Al- and Mg-based substrates was studied by two imaging techniques-namely, SEM and computer microtomography. Two approaches for porosity determination were chosen; relatively simple and fast SEM surface and cross-sectional imaging was compared with X-ray micro computed tomography (microCT) rendering. Differences between 2D and 3D porosity were demonstrated and explained. A more compact PEO coating was found on the Al substrate, with a lower porosity compared to Mg substrates under the same processing parameters. Furthermore, huge pore clusters were detected with microCT. Overall, 2D surface porosity calculations did not show sufficient accuracy for them to become the recommended method for the exact evaluation of the porosity of PEO coatings; microCT is a more appropriate method for porosity evaluation compared to SEM imaging. Moreover, the advantage of 3D microCT images clearly lies in the detection of closed and open porosity, which are important for coating properties